Enhanced survey information synthesis

ABSTRACT

Enhanced survey information synthesis can include performing a respondent assessment of a survey respondent based on respondent data obtained electronically from one or more electronic data sources. Survey responses provided by the survey respondent can be adjusted, the adjusting based on the respondent assessment. A revised survey can be generated, the revised survey comprising the survey responses adjusted based on the respondent assessment.

BACKGROUND

This disclosure relates to electronic data processing, and moreparticularly, to processing electronic survey data.

Surveys are widely used by researchers in a broad range of fields,including academia, business, government, and a host of others. Surveysare an efficient way—at times, the only way—to elicit certain types ofinformation from specific groups on behalf of advertisers,psychologists, political candidates, public officials, marketers,sociologists, and many others. Typically comprising a list of questions,a survey can take various forms (e.g., census, opinion poll, householdsurvey) and can be conducted in myriad ways. Surveys have long beenconducted in one-on-one interviews, by phone, and via mail. Morerecently, surveys are conducted on-line via data communication networksand the Internet. Regardless of the manner in which survey data iscollected, virtually all survey data is now analyzed using some form ofelectronic data processing.

SUMMARY

In one or more embodiments, a method includes performing, with computerhardware, a respondent assessment of a survey respondent based onrespondent data obtained electronically from at least one electronicdata source. The method also can include adjusting, with the computerhardware, survey responses provided by the survey respondent, whereinthe adjusting is based on the respondent assessment. Additionally, themethod can include generating a revised survey with the computerhardware, the revised survey comprising the survey responses adjustedbased on the respondent assessment.

In one or more embodiments, a system includes a processor configured toinitiate operations. The operations can include performing a respondentassessment of a survey respondent based on respondent data obtainedelectronically from at least one electronic data source. The operationsalso can include adjusting survey responses provided by the surveyrespondent, wherein the adjusting is based on the respondent assessment.Additionally, the operations can include generating a revised survey,the revised survey comprising the survey responses adjusted based on therespondent assessment.

In one or more embodiments, a computer program product includes acomputer readable storage medium having program instructions storedthereon. The program instructions are executable by a processor toinitiate operations. The operations can include performing, with theprocessor, a respondent assessment of a survey respondent based onrespondent data obtained electronically from at least one electronicdata source. The operations also can include adjusting, with theprocessor, survey responses provided by the survey respondent, whereinthe adjusting is based on the respondent assessment. Additionally, theoperations can include generating a revised survey with the processor,the revised survey comprising the survey responses adjusted based on therespondent assessment.

This Summary section is provided merely to introduce certain conceptsand not to identify any key or essential features of the claimed subjectmatter. Other features of the inventive arrangements will be apparentfrom the accompanying drawings and from the following detaileddescription.

BRIEF DESCRIPTION OF THE DRAWINGS

The inventive arrangements are illustrated by way of example in theaccompanying drawings. The drawings, however, should not be construed tobe limiting of the inventive arrangements to only the particularimplementations shown. Various aspects and advantages will becomeapparent upon review of the following detailed description and uponreference to the drawings.

FIG. 1 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 2 depicts abstraction model layers according to an embodiment ofthe present invention.

FIG. 3 depicts a cloud computing node according to an embodiment of thepresent invention.

FIG. 4 depicts a system for enhanced survey information synthesisaccording to an embodiment of the invention.

FIG. 5 depicts an example neural network used in a system for enhancedsurvey information synthesis according to an embodiment of theinvention.

FIG. 6 is a flowchart of a method for enhanced survey informationsynthesis according to an embodiment of the invention.

DETAILED DESCRIPTION

While the disclosure concludes with claims defining novel features, itis believed that the various features described within this disclosurewill be better understood from a consideration of the description inconjunction with the drawings. The process(es), machine(s),manufacture(s) and any variations thereof described herein are providedfor purposes of illustration. Specific structural and functional detailsdescribed within this disclosure are not to be interpreted as limiting,but merely as a basis for the claims and as a representative basis forteaching one skilled in the art to variously employ the featuresdescribed in virtually any appropriately detailed structure. Further,the terms and phrases used within this disclosure are not intended to belimiting, but rather to provide an understandable description of thefeatures described.

This disclosure relates to electronic data processing, and moreparticularly, to processing electronic survey data. Notwithstanding theundeniable usefulness of survey data, the reliability of survey data canbe diminished by what can be termed the “human dimension.” The humandimension refers broadly to psychological, experiential, educational,emotional, or other factors, specific to a survey respondent, which canaffect the reliability or usefulness of the survey respondent's answerto a given survey question.

For example, a survey respondent may be affected by an inherentsubjectivity or cognitive bias that results in a perceptual distortion,inaccurate judgment, illogical interpretation, or other so-calledirrationality that, though perhaps minor, nonetheless reduces thereliability of the survey respondent's answer to a survey question. Forexample, the survey respondent's answer to a public policy question maybe affected by the survey respondent's party affiliation. Even atransitory emotional state may affect the survey respondent's answer toa survey question. For example, a survey question dealing with an issuethat has very recently been the subject of news headlines may engenderintense feelings that affect the survey respondent's objectivity inresponding to the question. Other factors, however, may make the surveyrespondent's response particularly reliable, at least relative to otherrespondents. For example, a survey respondent's response to a questionon environmental policy is likely to be especially pertinent and usefulrelative to others' responses if the survey respondent is anenvironmental scientist.

In accordance with the inventive arrangements disclosed herein, surveyinformation is enhanced through a synthesis of survey responses and anassessment of the survey respondent who provides the responses. Themethods, systems, and computer program products disclosed herein canadjust survey responses based on an assessment of the survey respondent.The respondent assessment can be based on various factors. The factorscan include attitudinal, behavioral, and psychological factors. Thefactors can include experiential and educational factors. That is,experiences, expertise, and/or education relevant to a survey topic thatmake a survey respondent's response especially useful and/or reliable.Such factors used for performing a respondent assessment can be obtainedfrom various electronic data sources, especially networked data sourcessuch as websites and social networking sites. Thus, the data sources caninclude various media, social feeds, interactive voice response (IVR)systems, on-line chats, and other publicly accessible data sourcesmaintained by the survey respondent or by one or more entities (e.g.,professional organizations, commercial enterprises, non-profit entities)with which the survey respondent is affiliated.

The respondent assessment can map the various respondent-specificfactors to survey question responses provided by the survey respondent.The mapping can, for example, assign weights to the survey respondent'sanswers to survey questions, the weights either reducing or enhancingthe effect that specific survey respondent's answers have on the overallresults of a survey. For example, the weights can reflect the expertiseand/or experiences of the survey respondent, as well as attitudes,behaviors, and personality traits. A respondent assessment can beperformed for each survey respondent, and the corresponding mapping ofrespondent-specific factors to the survey respondents' answers canenhance the reliability of the survey.

Another aspect of the embodiments disclosed herein is enhancement of thefunctioning of a data processing system itself, when the system used tocollect and analyze survey data. The data processing system's enhancedefficiency can be enhanced by limiting the survey questions posed toonly those specific to the topic of the survey, thus avoiding anelaborate set of questions specific to or about the particular surveyrespondent. Instead of having to elicit an extensive list ofrespondent-specific questions for processing, the data necessary forperforming the respondent assessment can be obtained by the dataprocessing system directly from other sites (e.g., social networkingsite).

Further aspects of the embodiments described within this disclosure aredescribed in greater detail with reference to the figures below. Forpurposes of simplicity and clarity of illustration, elements shown inthe figures have not necessarily been drawn to scale. For example, thedimensions of some of the elements may be exaggerated relative to otherelements for clarity. Further, where considered appropriate, referencenumbers are repeated among the figures to indicate corresponding,analogous, or like features.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementations of theteachings recited herein are not limited to a cloud computingenvironment. Rather, embodiments of the present invention are capable ofbeing implemented in conjunction with any other type of computingenvironment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 1 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 2, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 1) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 2 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA. Workloads layer 90 provides examples offunctionality for which the cloud computing environment may be utilized.Examples of workloads and functions which may be provided from thislayer include: mapping and navigation 91; software development andlifecycle management 92; virtual classroom education delivery 93; dataanalytics processing 94; transaction processing 95; and a system forenhanced survey information synthesis 96.

In one or more embodiments, the system for enhanced survey informationsynthesis 96 is capable of performing a respondent assessment of asurvey respondent who responds to a survey by answering one or moresurvey questions. The respondent assessment can be based onrespondent-specific data that can include data related to a surveyrespondent's experiences relevant to a survey topic, data regarding thesurvey respondent's education level, data concerning the surveyrespondent's attitudes regarding subject matter relevant to the surveytopic. Respondent data can include social activities of the surveyrespondent. Respondent data can indicate respondent-specific factorssuch as personality traits, and/or behavioral attributes.

The system for enhanced survey information synthesis 96 is capable ofobtaining the respondent data electronically from one or more electronicdata sources. The electronic data sources can be networked data sources,that is, sources that are communicatively coupled to an electronic datanetwork (e.g., local area network or wide area network) or the Internet.Such sources thus can include websites (e.g., business, professional,and other organization websites), social networking sites, and othernetworked electronic data sources.

The system for enhanced survey information synthesis 96 is capable ofadjusting survey responses provided by the survey respondent based onthe respondent assessment. The respondent assessment can be performedusing a classification model. The classification model can beconstructed using machine learning. The classification model, forexample, can be a deep learning neural network. The respondentassessment can be based on other models constructed using machinelearning. The other machine learning models can be supervised orunsupervised learning models. For example, the machine learning modelcan be based on the k-nearest neighbors, using different distancemetrics. In other embodiments, the respondent assessment canalternatively, or additionally, comprise determining an emotional tonebased on the survey responses of the survey respondent.

The system can generate a revised survey that comprises the surveyresponses adjusted based on the respondent assessment. The adjustedsurvey responses are adjusted according to an assessment of the surveyrespondents. Accordingly, the adjusted survey responses arestatistically more reliable and/or more useful. Further features of thesystem for enhanced survey information synthesis 96 are described belowin greater detail.

FIG. 3 illustrates a schematic of an example of a computing node 300. Inone or more embodiments, computing node 300 is an example of a suitablecloud computing node. Computing node 300 is not intended to suggest anylimitation as to the scope of use or functionality of embodiments of theinvention described herein. Computing node 300 is capable of performingany of the functionality described within this disclosure.

Computing node 300 includes a computer system 312, which is operationalwith numerous other general-purpose or special-purpose computing systemenvironments or configurations. Examples of well-known computingsystems, environments, and/or configurations that may be suitable foruse with computer system 312 include, but are not limited to, personalcomputer systems, server computer systems, thin clients, thick clients,hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, set top boxes, programmable consumerelectronics, network PCs, minicomputer systems, mainframe computersystems, and distributed cloud computing environments that include anyof the above systems or devices, and the like.

Computer system 312 may be described in the general context of computersystem-executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.Computer system 312 may be practiced in distributed cloud computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed cloudcomputing environment, program modules may be located in both local andremote computer system storage media including memory storage devices.

As shown in FIG. 3, computer system 312 is shown in the form of ageneral-purpose computing device. The components of computer system 312may include, but are not limited to, one or more processors 316, amemory 328, and a bus 318 that couples various system componentsincluding memory 328 to processor 316.

Bus 318 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus,Peripheral Component Interconnect (PCI) bus, and PCI Express (PCIe) bus.

Computer system 312 typically includes a variety of computer systemreadable media. Such media may be any available media that is accessibleby computer system 312, and may include both volatile and non-volatilemedia, removable and non-removable media.

Memory 328 may include computer system readable media in the form ofvolatile memory, such as random-access memory (RAM) 330 and/or cachememory 332. Computer system 312 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example, storage system 334 can be provided for readingfrom and writing to a non-removable, non-volatile magnetic media and/orsolid-state drive(s) (not shown and typically called a “hard drive”).Although not shown, a magnetic disk drive for reading from and writingto a removable, non-volatile magnetic disk (e.g., a “floppy disk”), andan optical disk drive for reading from or writing to a removable,non-volatile optical disk such as a CD-ROM, DVD-ROM or other opticalmedia can be provided. In such instances, each can be connected to bus318 by one or more data media interfaces. As will be further depictedand described below, memory 328 may include at least one program producthaving a set (e.g., at least one) of program modules that are configuredto carry out the functions of embodiments of the invention.

Program/utility 340, having a set (at least one) of program modules 342,may be stored in memory 328 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 342 generally carry out the functionsand/or methodologies of embodiments of the invention as describedherein. For example, one or more of the program modules may include asystem for enhanced survey information synthesis 96 or portions thereof.

Program/utility 340 is executable by processor 316. Program/utility 340and any data items used, generated, and/or operated upon by computersystem 312 are functional data structures that impart functionality whenemployed by computer system 312. As defined within this disclosure, a“data structure” is a physical implementation of a data model'sorganization of data within a physical memory. As such, a data structureis formed of specific electrical or magnetic structural elements in amemory. A data structure imposes physical organization on the datastored in the memory as used by an application program executed using aprocessor.

Computer system 312 may also communicate with one or more externaldevices 314 such as a keyboard, a pointing device, a display 324, etc.;one or more devices that enable a user to interact with computer system312; and/or any devices (e.g., network card, modem, etc.) that enablecomputer system 312 to communicate with one or more other computingdevices. Such communication can occur via input/output (I/O) interfaces322. Still yet, computer system 312 can communicate with one or morenetworks such as a local area network (LAN), a general wide area network(WAN), and/or a public network (e.g., the Internet) via network adapter320. As depicted, network adapter 320 communicates with the othercomponents of computer system 312 via bus 318. It should be understoodthat although not shown, other hardware and/or software components couldbe used in conjunction with computer system 312. Examples, include, butare not limited to: microcode, device drivers, redundant processingunits, external disk drive arrays, RAID systems, tape drives, and dataarchival storage systems, etc.

While computing node 300 is used to illustrate an example of a cloudcomputing node, it should be appreciated that a computer system using anarchitecture the same as or similar to that described in connection withFIG. 3 may be used in a non-cloud computing implementation to performthe various operations described herein. In this regard, the exampleembodiments described herein are not intended to be limited to a cloudcomputing environment. Computing node 300 is an example of a dataprocessing system. As defined herein, the term “data processing system”means one or more hardware systems configured to process data, eachhardware system including at least one processor programmed to initiateoperations and memory.

Computing node 300 is an example of computer hardware. Computing node300 may include fewer components than shown or additional components notillustrated in FIG. 3 depending upon the particular type of deviceand/or system that is implemented. The particular operating systemand/or application(s) included may vary according to device and/orsystem type as may the types of I/O devices included. Further, one ormore of the illustrative components may be incorporated into, orotherwise form a portion of, another component. For example, a processormay include at least some memory.

Computing node 300 is also an example of a server. As defined herein,the term “server” means a data processing system configured to shareservices with one or more other data processing systems. As definedherein, the term “client device” means a data processing system thatrequests shared services from a server, and with which a user directlyinteracts. Examples of a client device include, but are not limited to,a workstation, a desktop computer, a computer terminal, a mobilecomputer, a laptop computer, a netbook computer, a tablet computer, asmart phone, a personal digital assistant, a smart watch, smart glasses,a gaming device, a set-top box, a smart television and the like. In oneor more embodiments, the various user devices described herein may beclient devices. Network infrastructure, such as routers, firewalls,switches, access points and the like, are not client devices as the term“client device” is defined herein.

FIG. 4 depicts one embodiment of system 400, which is similar to systemfor enhanced survey information synthesis 96 described in reference toFIG. 2. System 400 illustratively includes source accessor 402,respondent assessor 404, response adjustor 406, and revised resultsgenerator 408 operatively coupled together. As described more fullybelow, for each survey respondent who responds to a survey by answeringone or more survey questions, respondent assessor 404 performs arespondent assessment. A respondent assessment assesses selectedattributes (e.g., experience, education, attitudes, personality traits,behavioral attributes) of a survey respondent. The respondent assessmentis based on respondent data obtained electronically by source accessor402 from one or more electronic data sources (e.g., social networkingsite), including networked data sources. Response adjustor 406 adjustssurvey responses provided by each survey respondent, the adjusting basedon the response assessment performed by respondent assessor 404 for eachsurvey respondent. A revised survey is generated by revised resultsgenerator 408. The revised survey comprises the survey responsesadjusted based on the respondent assessments of each of the surveyrespondents.

In one embodiment, system 400 is implemented in computersystem-executable instructions (e.g., one or more program modules) thatare executable by a processor such as processor 316 of computer system312 described in reference to FIG. 3. Accordingly, system 400 can beimplemented, for example, in computer-system instructions executable ona server (e.g., cloud-based server) or other type of computer system. Inother embodiments, one or more of source accessor 402, respondentassessor 404, response adjustor 406, and revised results generator 408can be implemented in hardwired circuitry or in a combination ofhardwired circuitry and computer system-executable instructions.

System 400 can be integrated in or communicatively coupled with one ormore survey sources, illustrated by survey source 410 in FIG. 4. Surveysource 410 can disseminate surveys, collect survey responses, andgenerate electronic survey data 412 that is conveyed to system 400.Survey source 410 can be any type of system for disseminating,collecting, and collating survey data. For example, survey source 410can be a publicly accessible kiosk for use by survey respondents. Surveysource 410, for example, can be a networked computing node or systemthat conducts on-line surveys. Survey source 410 can comprise aspeech-to-text engine (not shown) for converting verbal responsescollected through phone interviews into electronic survey data 412, forexample. Survey source 410, for example, can comprise an opticalcharacter reader (not shown) for converting mail-in survey responses orother pen-and-paper responses (e.g., based on in-person interviews) intoelectronic survey data 412. Survey source 410, for example, can be awearable device or an Internet-of-Things (IOT) device via which surveyrespondents can convey electronic survey data 412 that is provided tosystem 400.

Revised survey results generated by revised results generator 408 can beconveyed as electronic survey data results 414 to survey resultsdisseminator 416. Survey results disseminator 416 can also be acomputing node or system, which can be integrated with orcommunicatively coupled to survey source 410 and which can conveyrevised survey results to one or more survey users. Revised surveyresults are survey results that are adjusted based on the respondentassessments performed by respondent assessor 404 for each of thesurvey's respondents.

Respondent assessments are performed by respondent assessor 404 based onrespondent-specific data such as data concerning a survey respondent'sexperiences (experiential data) relevant to a survey topic, educationlevel or other educational data, expressed attitudes, personalitytraits, and/or behavioral attributes. Respondent-specific data can beprovided by a survey respondent himself or herself directly along withanswers to questions relating to the survey topic. For example,respondent-specific data can be determined based on a surveyrespondent's answers to a test such as the Myers-Briggs Type Indicator(MBTI) or Process Communication Model (PCM). Alternatively, oradditionally, respondent-specific data that is not provided by thesurvey respondent himself or herself, but is nonetheless relevant forperforming a respondent assessment can be obtained by source accessor402 from various electronic data sources 418, including networked datasources such as social networking sites and other on-line data sourcesthat are communicatively coupled with system 400 via one or more datacommunication networks illustrated by data communications network 420.For example, various techniques can be incorporated into respondentassessor 404 to predict personality traits based on public informationthat survey respondents provide on social media. Techniques based onOpen-Vocabulary Analysis can be applied to a survey respondent'scomments and chats on social media, for example. Open-VocabularyAnalysis analyzes words shown to be predictive of personality traits andcan be used in lieu of or in addition to other survey questions (e.g.,MBTI or PCM questions) addressed directly to the survey respondent.Other techniques incorporated into respondent assessor for predictingpersonality traits include, for example, machine learning algorithms(e.g., Gaussian Processes) that have been shown to predict certainpersonality traits with relatively high accuracy using publicinformation that survey respondents provide on social networking sitesand other on-line data sources.

As described in greater detail below, such electronic data sourcesaccessed by source accessor 402 can be a source for on-line chats,comments, descriptions of experiences and/or education, expressions ofattitudes, and the like all related to a specific survey respondent. Inone embodiment, also described in greater detail below, suchrespondent-specific information can provide inputs to various machinelearning models that, in certain embodiments, can be incorporated inrespondent assessor 404 for performing respondent assessments that, inpart, classify a survey respondent. Response adjustor 406 can adjust thesurvey respondent's responses to survey questions by adjusting theresponses (e.g., weighting the responses) based on the classification.

Data communications network 420 can provide communication links betweenvarious devices and data processing systems. The communication links caninclude connections, such as wired communication links, wirelesscommunication links, or fiber optic cables, and can be implemented as,or include, one or more (or any combination of) different communicationtechnologies such as a Wide Area Network (WAN), a Local Area Network(LAN), a wireless network (e.g., a wireless WAN and/or a wireless LAN),a mobile or cellular network, a Virtual Private Network (VPN), theInternet, a Public Switched Telephone Network (PSTN), and so forth.Devices capable of coupling to data communications network 420 via wiredand/or wireless communication links can include personal computers,portable computing or communication devices, network computers, tabletcomputers, mobile phones, or the like.

As defined herein, the term “communication link” means a mode ofcommunication using one or more electronic devices. A communication linkis associated with a specific format and/or communication protocol forsending messages. For example, using a specific format and/orcommunication protocol, an electronic device can send a message toanother electronic device as a text message, an email, a video call, avoice call, and/or a post to a social networking system. A communicationlink for exchanging text messages is considered a distinct communicationlink. Likewise, a communication link for exchanging emails is a distinctcommunication, as is a communication link for video calls, as is acommunication link for voice calls. So, too, a communication link usedfor posting to social networking systems is considered a distinctcommunication link. That is, each type of communication linkcorresponding to a different type or mode of communication is considereda distinct communication link.

Over one or more such communication links, source accessor 402 canaccess one or more electronic data sources 418 to obtain respondent dataused by respondent assessor 404 for performing a respondent assessmentof a survey respondent. In certain instances, a survey respondent canindicate one or more electronic data sources 418. Alternatively, oradditionally, source accessor can identify one or more electronic datasources 418 that provides respondent-specific information and that ismade publicly available or accessible by the permission of a surveyrespondent (e.g., a survey respondent's website or social networkingsite). A business or other organization's website that includesinformation regarding a particular expertise of the survey respondent,for example, can be among the one or more electronic data sources 418.Among the one or more electronic data sources 418 can be a professionalorganization (e.g., medical association), for example, which includesinformation regarding the education and/or experience of the surveyrespondent. Electronic data sources 418 can include various othernetworked sites that can be identified with a survey respondent based onpublicly available information and that can provide educational,experiential, and/or attitudinal data regarding the survey respondent.

Social networking systems can also be among electronic data sources 418.Thus, respondent-specific data can be obtained by source accessor 402from exchanges involving the survey respondent over one or more suchsocial networking systems. A social networking system can be implementedas one or more interconnected computer systems, e.g., servers. Asdefined herein, a “social networking system” is a computing platformthat allows users to build social networks or social relations withother users who share similar interests, activities, backgrounds, and/orreal-life connections. Through a social networking system, users cansend communications through different mechanisms such as by postingmessages or other media, commenting on messages, posts, or other media,replying to messages, and performing other operations such as “liking” acommunication or item of media, sharing the communication or item ofmedia, expressing an emotional sentiment, and so forth. In the contextof a social networking system, actions such as posting, replying,liking, sharing, expressing sentiment, and so forth are programmaticactions that are monitored and persisted within the social networkingsystem, such as in a data structure within a data storage device withinand/or accessible by the social networking system.

Accordingly, source accessor 402 can obtain respondent-specific datafrom one or more social networking systems. Respondent assessor 404 canperform a respondent assessment based on the survey respondent'sexperiences, interests, activities, attitudes, and other informationthat shared by the survey respondent the one or more social networkingsystems. Respondent assessor 404 can perform a respondent assessment,for example, based on a survey respondent's communications throughdifferent mechanisms, such as by posting messages or other media,commenting on messages, posts, or other media, replying to messages, andperforming other operations such as “liking” a communication or item ofmedia, sharing the communication or item of media, expressing anemotional sentiment, and so forth.

Respondent assessor 404 can apply different techniques to the same typesof respondent-specific information for performing a personalityassessment (e.g., intuition, thinking, sensations, feelings) of a surveyrespondent, for example. A personality assessment made by respondentassessor 404 can be based on data obtained by source accessor 402,including social comments, on-line conversations, social activities,experiences with specific subject matter, geography, and other factors.

The respondent assessment performed by respondent assessor 404 is usedby response adjustor 406 to adjust the survey respondent's answers tosurvey questions. Adjusting by response adjustor 406 can comprisemultiplying a survey response (e.g., score based on a rating scale) by apredetermined weight (e.g., a real number). The weight applied to asurvey response value can be a value computed based on one or morerespondent-specific factors derived from the various types ofrespondent-specific information described above. The factors caninclude, for example, behavioral attributes or personality traits of asurvey respondent, the emotional tone of the survey respondent'sresponses to survey questions, experience of the survey respondent withrespect to a survey topic, expertise of the survey respondent withrespect to the survey topic, education level of the survey respondent,and/or other factors relevant to the survey topic.

A weight determined by response adjustor 406 can be a composite value.For example, if a respondent assessment of a survey respondentdetermines that the survey respondent has considerable education orexperience relevant to a survey topic, then absent any otherconsideration, a weight greater than one (e.g., 1.5) can apply to thesurvey respondent's answers on that topic. If, however, a tone analysisindicates, for example, that the same survey respondent exhibits angeror tentativeness (e.g., reluctance to answer), then a weight less thanone can apply (e.g., 0.5). An average or other formula can be used byresponse adjustor 406 to determine a composite value (e.g.,(1.5+0.5)/2=1.0) of the weight applied to the particular question.

Various other statistical formulas and calculations can be used byresponse adjustor 406 for adjusting survey results based on respondentassessments of survey respondents. One calculation that can be used byresponse adjustor 406 comprises adding predetermined weights to a surveyrespondent's answers to a question based on the survey respondent'sexperience. Greater weight is afforded the responses of surveyrespondents having greater experience with respect to a survey topic onthe assumption that survey respondents who have greater experienceprovide correspondingly more meaningful statistical data.

For example, in a survey conducted among employees of an organization, asurvey question may ask an employee's opinion about the organization.The survey can use a rating scale (e.g., Likert scale) for ranking thesurvey respondent's attitude to a survey statement, such as 1 for“strongly disagree,” 2 for “disagree,” 3 for “neutral,” 4 for “agree,”and 5 for “strongly agree.” Table 1, below, is an example of resultsobtained on a survey question from 14 survey respondents. Each responseis counted as “one” if the respondent's survey response is a 4 or a 5(“agrees” or “strongly agrees,” respectively, with a favorable statementabout the organization); and otherwise is counted as “zero” if therespondent's survey response is a 1, 2, or 3 (“disagrees,” “stronglyagrees,” or is “neutral,” respectively, with a favorable statement aboutthe organization). The summation of ones and zeros (raw count)determines the number of favorable views.

The results are adjusted by response adjustor 406 based on the followingweights corresponding to a survey respondent's years of experience withthe organization:

Over 10 years count = 0 or 1 weight = 1.8; 5 to 10 years count = 0 or 1weight = 1.6; 2 to 4 years count = 0 or 1 weight = 1.4; 1 to 3 yearscount = 0 or 1 weight = 1.2; and Less than 1 year count = 0 or 1 weight= 1.0.

The percentage favorable (percentage of respondents who selected either4 or 5 on the rating scale) is 43% using the raw count of zeros andones. When the survey responses are adjusted according to respondents'years of experience using the predetermined weights, revised resultsgenerator 408 determines that the percent of favorable responsesincreases 12 basis points.

TABLE 1 Scale Count of Favorable Count times Experience SelectionResponses (4 or 5) Weight 1 to 3 years 2 0 0 1 to 3 years 1 0 0 2 to 4years 4 1 1.4 2 to 4 years 5 1 1.4 5 to 10 years 4 1 1.6 5 to 10 years 41 1.6 Less than 1 year 1 0 0 Less than 1 year 2 0 0 Less than 1 year 2 00 Less than 1 year 1 0 0 Less than 1 year 1 0 0 Less than 1 year 2 0 0over 10 4 1 1.8 over 10 5 1 1.8 Count 14 17.6 Total 6 9.6 % Favorable43% 55%

Respondent assessor 404 optionally can include machine learning model422. Machine learning model 422 can be a classification or regressionmodel that is trained using supervised or unsupervised learning. Machinelearning model 422 in one embodiment can be a deep learning neuralnetwork for classifying survey respondents based on respondent-specificfactors (e.g., education, experience, behavioral attributes, personalitytraits). Using a deep learning neural network, respondent assessor 404can classify a survey respondent into one of n-classes. A weight vectorw_(i), or m-tuple (assuming a survey comprising m questions), drawn froma set of weight vectors (each weight vector corresponding to one of then-classes) can be applied to a survey respondent's answers to surveyquestions, the specific m-dimensional weight vector selected based onwhich of the n-classes the survey respondent is assigned by the deeplearning neural network.

Referring additionally to FIG. 5, neural network 500 illustrates anexample machine learning model comprising a neural network forclassifying a survey respondent. Input to neural network 500 can be inthe form of feature vectors, each feature vector corresponding to aspecific survey respondent. Each element of a feature vector correspondsto an attribute of the corresponding survey respondent. For example, oneelement may take on a value of zero or one, depending on whether thesurvey respondent exhibits a particular characteristic (e.g.,personality trait) as determined by a respondent assessment of thesurvey respondent. Another feature vector element, for example, can be apositive number representing the survey respondent's years of experiencewith respect to a survey topic. Another feature vector element canindicate the survey respondent's level of education, for example.

Moreover, elements of feature vectors can be based on social networkingchats, on-line comments, expressions of attitudes and opinions gleanedfrom social networking and other on-line messaging. The features canprovide useful indicia for classifying a survey respondent as part of arespondent assessment. Because such features, initially, are in the formof text, the text can be transformed into numerical tensors(multidimensional algebraic objects or one-dimensional vectors) bybreaking the text (e.g., words, characters, n-grams) into tokens andassociating numeric vectors with each. A technique such as categoricalencoding (one-hot encoding) or word embedding can be used to transformtext into numerical tensors that can input into neural network 500.

Neural network 500 is illustratively a deep learning neural networkcomprising a directed, acyclic graph of layers (text processing modulesor filters) 502A through 502M sequentially chained together. Output 504generated by neural network 500 is based on an input vector (featurevector) that feeds through layers 502A through 502M, each layermodifying the output of a preceding layer based on a set of parameters(or classification weights) 506A through 506M. The parameters orclassification weights (kernel and bias attributes) are trained(iteratively refined) using training samples fed into the neuralnetwork. The output generated is compared with true values (correctlylabeled survey respondent classifications) 510 of the training samples508. The difference between the generated values and true values 510 forclassifying the training samples 508 can be measured by a loss, which iscalculated by loss function 512. In one embodiment, loss function 512 isthe categorical cross-entropy criterion. In a feedback fashion,optimizer 514 adjusts weights 506A through 506M over successiveiterations using the backpropagation algorithm. The backpropagationalgorithm iteratively adjusts weights 506A through 506M in directionsthat lower the loss calculated by loss function 512. The iterativerefinement of weights 506A through 506M continues until an acceptablelevel of accuracy is achieved in classifying a separate test set ofsurvey respondent samples.

Once trained, neural network 500 classifies a survey respondent featurevector by outputting a vector whose elements are a probability (betweenzero and one) that the survey respondent represented by the featurevector belongs to a specific category or class. The survey respondent isclassified as belonging to the category for which the probability isgreatest and, depending on the classification, response adjustor 406applies a corresponding set of weights to each survey response providedby the model-classified survey respondent.

In another embodiment, machine learning model 422 can be an unsupervisedlearning model that groups survey respondents into distinct groups basedon model-identified similarities among of the survey respondents. Forexample, machine learning model 422 can be constructed based on thek-nearest neighbors, a non-parametric learning algorithm. Surveyrespondents can be grouped based on a closeness of the surveyrespondents' corresponding feature vectors, the elements of which arethe attributes or respondent-specific factors (e.g., education,experience, attitudes, emotion, behavioral attributes, personalitytraits) described above.

The closeness can be measured based on a Euclidean distance between thefeature vectors (in bold):

d(x _(i) , x _(j))=√{square root over (Σ_(k=1) ^(n)(x _(i) ^((k)) −x_(j) ^((k)))²)},

where x_(i)=(x_(i) ⁽¹⁾, x_(i) ⁽²⁾, . . . , x_(i) ^((n)))) and x_(j)=(x_(j) ⁽¹⁾, x_(j) ⁽²⁾, . . . , x_(j) ^((n))) are each n-dimensionalfeature vectors. An alternative metric for measuring closeness can bebased on the feature vectors' cosine similarity:

${\cos \left( {\angle \left( {x_{i},x_{j}} \right)} \right)} = {\frac{\sum_{k = 1}^{n}{x_{i}^{(k)}x_{j}^{(k)}}}{\sqrt{\sum_{k = 1}^{n}\left( x_{i}^{(k)} \right)^{2}}\sqrt{\sum_{k = 1}^{n}\left( x_{j}^{(k)} \right)^{2}}}.}$

Other distance metrics for determining closeness can be used, such asChebychev distance, Mahalanobis, distance, and Hamming distance. Thesame predetermined weights can be used to adjust the survey responses ofsurvey respondents that, based on closeness of corresponding featurevectors, are grouped together. For each distinct group of surveyrespondents, a particular set of weights can be applied with respect tothe survey responses of those members of the same group.

In other embodiments, respondent assessor 404 can include still othertypes of machine learning models for performing the respondentassessments that are based on respondent-specific factors (e.g.,education, experiences, attitudes, personality traits, behavioralattributes) and that are the basis for adjusting survey responsesprovided by the survey respondents. Alternatively, or additionally,respondent assessor 404 optionally can include statistical analyzer 424for determining appropriate weights based on different types ofstatistical analyses (e.g., linear regression, ordinary least squaresregression, nonparametric regression) of the respondent-specific factors(e.g., education, experiences, attitudes, personality traits, behavioralattributes). Based on machine learning classification and/or statisticalanalysis, respondent assessor 404 can determine an average, ornormalized score, based on which the response adjustor 406 can weighteach response given by each survey respondent based on a correspondingrespondent assessment derived from respondent-specific factors orattributes.

In still other embodiments, respondent assessor 404 optionally canadditionally, or alternatively, include tone analyzer 426. Tone analyzer426 can analyze a survey respondent's responses (written or textualrenderings of verbal responses) using linguistic analysis to determinethe survey respondent's tone (e.g., frustrated, fearful, sad, satisfied,excited, polite, impolite, sympathetic, angry, analytical) at thesentence level. A machine learning model can train tone analyzer 426 topredict tones based on several categories of features including n-gramfeatures, lexical features from different dictionaries, punctuation, andsecond-person references. The machine learning model, in one embodiment,can comprise a Support Vector Machine (SVM).

Tone analyzer 426, in another embodiment, optionally can incorporatecapabilities for ascertaining a survey respondent's emotion or tone fromthe survey respondent's voice-recorded answers to survey questions. Inaccordance with the embodiment, tone analyzer 426 can combinespeech-to-text technology with a tone analyzer that measures the surveyrespondent's emotion based on speech output, either in real-time orbased on recorded speech. Accordingly, coupling system 400 with a voiceresponse system, tone analyzer 426 can perform tone analysis on voiceresponses to survey questions.

A survey respondent's tone can be an adjunct to, or an alternative for,other respondent-specific attributes or factors used for performing arespondent assessment that is the basis for adjusting survey responsesprovided by the survey respondent. For example, a survey response madeby a survey respondent—regardless of the survey respondent's experience,education, personality traits, or other attributes—is likely to be lessreliable if made when the survey respondent is angry. Conversely, asurvey response made when the same survey respondent is determined to bein an analytical frame of mind is correspondingly more likely to bereliable. A survey respondent's tone determined by tone analyzer 426 canbe incorporated in or used as an alternative to otherrespondent-specific factors for performing a respondent assessment forthe survey respondent. For example, survey responses (answers to surveyquestions) of a survey respondent whose tone is, for example, angry orfrustrated can be given less weight that the responses otherwise wouldbe given were the survey respondent's tone not negative.

FIG. 6 is a flowchart of method 600 for enhanced survey informationsynthesis, according to one embodiment. Method 600 can be performed by asystem the same as or similar to the systems described in reference toFIGS. 1-5. The system at block 602 can perform a respondent assessmentof a survey respondent who responds to a survey by answering one or moresurvey questions.

The respondent assessment can be based on respondent data. Respondentdata can include data related to a survey respondent's experiences(experiential data) relevant to a survey topic, for example. Respondentdata, for example, can include data regarding the survey respondent'seducation level or other educational data. Respondent data can concernthe survey respondent's attitude regarding subject matter relevant tothe survey topic. Respondent data can include social activities of thesurvey respondent. Respondent data can indicate respondent-specificfactors such as personality traits, and/or behavioral attributes. Therespondent data can be obtained by the system electronically from one ormore electronic data sources, such as websites, social networking sites,and other networked electronic data sources.

At block 604, the system can adjust survey responses provided by thesurvey respondent. The adjusting can be based on the respondentassessment. The system, at block 606 can generate a revised survey thatcomprises the survey responses adjusted based on the respondentassessment.

In one embodiment, the respondent assessment can be based on aclassification model. The classification model can be constructed usingmachine learning. The classification model, for example, can be a deeplearning neural network. The respondent assessment can be based on othermodels constructed using machine learning. The other machine learningmodels can be supervised or unsupervised learning models. For example,the machine learning model can be based on the k-nearest neighbors,using different distance metrics. In other embodiments, the respondentassessment can alternatively, or additionally, comprise determining anemotional tone based on the survey responses of the survey respondent.

Any combination of attributes such as those described herein cab be usedto determine whether and how to adjust a survey respondent's response toone or more survey questions. In another aspect, the responses can bebased on a particular selected one or selected combination of attributesthat can affect the usefulness and/or the one or more questions.Moreover, in still another aspect, particular attributes used can varyfrom one survey question to another in any survey.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. Notwithstanding,several definitions that apply throughout this document now will bepresented.

As defined herein, the terms “at least one,” “one or more,” and“and/or,” are open-ended expressions that are both conjunctive anddisjunctive in operation unless explicitly stated otherwise. Forexample, each of the expressions “at least one of A, B and C,” “at leastone of A, B, or C,” “one or more of A, B, and C,” “one or more of A, B,or C,” and “A, B, and/or C” means A alone, B alone, C alone, A and Btogether, A and C together, B and C together, or A, B and C together.

As defined herein, the terms “includes,” “including,” “comprises,”and/or “comprising,” specify the presence of stated features, integers,steps, operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof.

As defined herein, the terms “if,” “when,” and “upon” mean “in responseto” or “responsive to,” depending upon the context. Thus, for example,the phrases “if it is determined” and “if [a stated condition or event]is detected” are to be construed to mean “in response to determining” or“in response to detecting [the stated condition or event]” or“responsive to determining” or “responsive to detecting [the statedcondition or event],” depending on the context.

As defined herein, the terms “one embodiment,” “an embodiment,” “in oneor more embodiments,” “in particular embodiments,” or similar languagemean that a particular feature, structure, or characteristic describedin connection with the embodiment is included in at least one embodimentdescribed within this disclosure. Thus, appearances of theaforementioned phrases and/or similar language throughout thisdisclosure may, but do not necessarily, all refer to the sameembodiment.

As defined herein, the term “output” means storing in physical memoryelements, e.g., devices, writing to display or other peripheral outputdevice, sending or transmitting to another system, exporting, or thelike.

As defined herein, the term “processor” means at least one hardwarecircuit configured to carry out instructions. The instructions may becontained in program instructions. The hardware circuit may be anintegrated circuit. Examples of a processor include, but are not limitedto, a central processing unit (CPU), an array processor, a vectorprocessor, a digital signal processor (DSP), a field-programmable gatearray (FPGA), a programmable logic array (PLA), an application specificintegrated circuit (ASIC), programmable logic circuitry, and acontroller.

As defined herein, the term “real time” means a level of processingresponsiveness that a user or system senses as sufficiently immediatefor a particular process or determination to be made, or that enablesthe processor to keep up with some external process.

As defined herein, the phrases “responsive to” and “in response to” meanresponding or reacting readily to an action or event. Thus, if a secondaction is performed “responsive to” or “in response to” a first action,there is a causal relationship between an occurrence of the first actionand an occurrence of the second action. The phrases “responsive to” and“in response to” indicate the causal relationship.

The term “substantially” means that the recited characteristic,parameter, or value need not be achieved exactly, but that deviations orvariations, including for example, tolerances, measurement error,measurement accuracy limitations, and other factors known to those ofskill in the art, may occur in amounts that do not preclude the effectthe characteristic was intended to provide.

As defined herein, the terms “user” and “survey respondent” mean a humanbeing. Accordingly, the terms “users” and “survey respondents” meanmultiple human beings.

The terms first, second, etc. may be used herein to describe variouselements. These elements should not be limited by these terms, as theseterms are only used to distinguish one element from another unlessstated otherwise or the context clearly indicates otherwise.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerreadable program instructions may also be stored in a computer readablestorage medium that can direct a computer, a programmable dataprocessing apparatus, and/or other devices to function in a particularmanner, such that the computer readable storage medium havinginstructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be accomplished as one step, executed concurrently,substantially concurrently, in a partially or wholly temporallyoverlapping manner, or the blocks may sometimes be executed in thereverse order, depending upon the functionality involved. It will alsobe noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions. The terminology used herein is for the purpose ofdescribing particular embodiments only and is not intended to belimiting of the invention. As used herein, the singular forms “a,” “an,”and “the” are intended to include the plural forms as well, unless thecontext clearly indicates otherwise. It will be further understood thatthe terms “includes,” “including,” “comprises,” and/or “comprising,”when used in this disclosure, specify the presence of stated features,integers, steps, operations, elements, and/or components, but do notpreclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration and are not intended tobe exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

1-7. (canceled)
 8. A system, comprising: a processor configured toinitiate operations including: performing a respondent assessment of asurvey respondent based on respondent data obtained electronically fromat least one electronic data source; adjusting survey responses providedby the survey respondent, wherein the adjusting is based on therespondent assessment; and generating a revised survey comprising thesurvey responses adjusted based on the respondent assessment.
 9. Thesystem of claim 8, wherein the respondent assessment is based on aclassification model constructed using machine learning.
 10. The systemof claim 9, wherein the classification model is a deep learning neuralnetwork.
 11. The system of claim 8, wherein the respondent assessmentcomprises determining behavioral attributes and/or personality traits ofthe survey respondent.
 12. The system of claim 8, wherein the respondentassessment comprises determining an emotional tone based on the surveyresponses of the survey respondent.
 13. The system of claim 8, whereinthe respondent data comprises at least one of experiential data,educational data, and/or social activities data.
 14. A computer programproduct comprising a computer readable storage medium having programinstructions embodied therewith, the program instructions executable bya processor to cause the processor to initiate operations comprising:performing, with the processor, a respondent assessment of a surveyrespondent based on respondent data obtained electronically from atleast one electronic data source; adjusting, with the processor, surveyresponses provided by the survey respondent wherein the adjusting isbased on the respondent assessment; and generating, with the processor,a revised survey comprising the survey responses adjusted based on therespondent assessment.
 15. The computer program product of claim 14,wherein the respondent assessment is based on a classification modelconstructed using machine learning.
 16. The computer program product ofclaim 15, wherein the classification model is a deep learning neuralnetwork.
 17. The computer program product of claim 14, wherein therespondent assessment comprises determining behavioral attributes and/orpersonality traits of the survey respondent.
 18. The computer programproduct of claim 14, wherein the respondent assessment comprisesdetermining an emotional tone based on the survey responses of thesurvey respondent.
 19. The computer program product of claim 14, whereinthe respondent data comprises at least one of experiential data,educational data, and/or social activities data.
 20. The computerprogram product of claim 14, wherein the electronic data sourcecomprises a networked electronic data source.